Psoriasis and Sleep Disturbance: A US Population-Based Study Using the NHANES Database.

INTRODUCTION: Psoriasis, a chronic inflammatory skin condition, affects approximately 3.0% of the US population, with patients often experiencing significant sleep disturbances. These disturbances include a higher prevalence of conditions such as obstructive sleep apnea, restless leg syndrome, and insomnia. Given the additional risks for cardiovascular disease, metabolic disorders, and depression linked to both poor sleep and psoriasis, addressing sleep issues in this patient group is critical. METHODS: The study utilized National Health and Nutrition Examination Survey (NHANES) data, focusing on individuals aged ≥ 20 years who provided information on psoriasis status and sleep. Multistage stratified survey methodology was applied, with multivariable logistic regression models used to examine the association between psoriasis and sleep issues, adjusting for factors such as age, gender, and health history. RESULTS: Psoriasis diagnosis was significantly associated with trouble sleeping (adjusted odds ratio [aOR] 1.88; 95% confidence interval [CI] 1.44-2.45). There was no significant association between psoriasis and sleep quantity. Older age, female gender, and a history of sleep disorders were predictors of trouble sleeping among psoriasis patients. CONCLUSIONS: Psoriasis is significantly associated with sleep disturbances, independent of sleep duration. This underscores the need for clinical screening focusing on sleep quality rather than quantity in psoriasis patients to effectively identify and treat sleep-related comorbidities. Further research using objective sleep measures is warranted to guide clinical management and improve patient quality of life.

Associations between Longitudinal Changes in Sleep Stages and Risk of Cognitive Decline in Older Men.

STUDY OBJECTIVES: To investigate the relationships between longitudinal changes in sleep stages and the risk of cognitive decline in older men. METHODS: This study included 978 community-dwelling older men who participated in the first (2003-2005) and second (2009-2012) sleep ancillary study visits of the Osteoporotic Fractures in Men Study. We examined the longitudinal changes in sleep stages at the initial and follow-up visits, and the association with concurrent clinically relevant cognitive decline during the 6.5-year follow-up. RESULTS: Men with low to moderate (quartile 2, Q2) and moderate increase (Q3) in N1 sleep percentage had a reduced risk of cognitive decline on the Modified Mini-Mental State Examination compared to those with a substantial increase (Q4) in N1 sleep percentage. Additionally, men who experienced a low to moderate (Q2) increase in N1 sleep percentage had a lower risk of cognitive decline on the Trails B compared with men in the reference group (Q4). Furthermore, men with the most pronounced reduction (Q1) in N2 sleep percentage had a significantly higher risk of cognitive decline on the Trails B compared to those in the reference group (Q4). No significant association was found between changes in N3 and rapid eye movement sleep and the risk of cognitive decline. CONCLUSIONS: Our results suggested that a relatively lower increase in N1 sleep showed a reduced risk of cognitive decline. However, a pronounced decrease in N2 sleep was associated with concurrent cognitive decline. These findings may help identify older men at risk of clinically relevant cognitive decline.

From Sleep Patterns to Heart Rhythms: Predicting Atrial Fibrillation from Overnight Polysomnograms.

Background: Atrial fibrillation (AF) is often asymptomatic and thus under-observed. Given the high risks of stroke and heart failure among patients with AF, early prediction and effective management are crucial. Importantly, obstructive sleep apnea is highly prevalent among AF patients (60-90%); therefore, electrocardiogram (ECG) analysis from polysomnography (PSG), a standard diagnostic tool for subjects with suspected sleep apnea, presents a unique opportunity for the early prediction of AF. Our goal is to identify individuals at a high risk of developing AF in the future from a single-lead ECG recorded during standard PSGs. Methods: We analyzed 18,782 single-lead ECG recordings from 13,609 subjects at Massachusetts General Hospital, identifying AF presence using ICD-9/10 codes in medical records. Our dataset comprises 15,913 recordings without a medical record for AF and 2,056 recordings from patients who were first diagnosed with AF between 1 day to 15 years after the PSG recording. The PSG data were partitioned into training, validation, and test cohorts. In the first phase, a signal quality index (SQI) was calculated in 30-second windows and those with SQI < 0.95 were removed. From each remaining window, 150 hand-crafted features were extracted from time, frequency, time-frequency domains, and phase-space reconstructions of the ECG. A compilation of 12 statistical features summarized these window-specific features per recording, resulting in 1,800 features. We then updated a pre-trained deep neural network and data from the PhysioNet Challenge 2021 using transfer-learning to discriminate between recordings with and without AF using the same Challenge data. The model was applied to the PSG ECGs in 16-second windows to generate the probability of AF for each window. From the resultant probability sequence, 13 statistical features were extracted. Subsequently, we trained a shallow neural network to predict future AF using the extracted ECG and probability features. Results: On the test set, our model demonstrated a sensitivity of 0.67, specificity of 0.81, and precision of 0.3 for predicting AF. Further, survival analysis for AF outcomes, using the log-rank test, revealed a hazard ratio of 8.36 (p-value of 1.93 × 10 -52 ). Conclusions: Our proposed ECG analysis method, utilizing overnight PSG data, shows promise in AF prediction despite a modest precision indicating the presence of false positive cases. This approach could potentially enable low-cost screening and proactive treatment for high-risk patients. Ongoing refinement, such as integrating additional physiological parameters could significantly reduce false positives, enhancing its clinical utility and accuracy.

Pioneering a Multi-Phase Framework to Harmonize Self-Reported Sleep Data Across Cohorts.

STUDY OBJECTIVES: Harmonizing and aggregating data across studies enable pooled analyses that support external validation and enhance replicability and generalizability. However, the multidimensional nature of sleep poses challenges for data harmonization and aggregation. Here we describe and implement our process for harmonizing self-reported sleep data. METHODS: We established a multi-phase framework to harmonize self-reported sleep data: (1) compile items; (2) group items into domains; (3) harmonize items; and (4) evaluate harmonizability. We applied this process to produce a pooled multi-cohort sample of five United States cohorts plus a separate yet fully harmonized sample from Rotterdam, Netherlands. Sleep and sociodemographic data are described and compared to demonstrate the utility of harmonization and aggregation. RESULTS: We collected 190 unique self-reported sleep items and grouped them into 15 conceptual domains. Using these domains as guiderails, we developed 14 harmonized items measuring aspects of Satisfaction, Alertness/Sleepiness, Timing, Efficiency, Duration, Insomnia, and Sleep Apnea. External raters determined that 13 of these 14 items had moderate-to-high harmonizability. Alertness/Sleepiness items had lower harmonizability, while continuous, quantitative items (e.g., timing, total sleep time, efficiency) had higher harmonizability. Descriptive statistics identified features that are more consistent (e.g., wake-up time, duration) and more heterogeneous (e.g., time in bed, bedtime) across samples. CONCLUSIONS: Our process can guide researchers and cohort stewards towards effective sleep harmonization and provides a foundation for further methodological development in this expanding field. Broader national and international initiatives promoting common data elements across cohorts are needed to enhance future harmonization and aggregation efforts.

Association of back pain with all-cause and cause-specific mortality among older men: a cohort study.

OBJECTIVE: We evaluated whether more severe back pain phenotypes-persistent, frequent or disabling back pain-are associated with higher mortality among older men. METHODS: In this secondary analysis of a prospective cohort, the Osteoporotic Fractures in Men (MrOS) study, we evaluated mortality rates by back pain phenotype among 5215 older community-dwelling men (mean age, 73 years, SD = 5.6) from six U.S. sites. The primary back pain measure used baseline and year five back pain questionnaire data to characterize participants as having: no back pain; non-persistent back pain; infrequent persistent back pain; or frequent persistent back pain. Secondary measures of back pain from year five questionnaire included disabling back pain phenotypes. The main outcomes measured were all-cause and cause-specific mortality. RESULTS: After the year five exam, during up to 18 years of follow-up (mean follow-up=10.3 years), there were 3513 deaths (1218 cardiovascular, 764 cancer, 1531 other). A higher proportion of men with frequent persistent back pain versus no back pain died (78% versus 69%; sociodemographic-adjusted HR = 1.27, 95%CI=1.11-1.45). No association was evident after further adjusting for health-related factors such as self-reported general health and comorbid chronic health conditions (fully-adjusted HR = 1.00; 95%CI=0.86-1.15). Results were similar for cardiovascular mortality and other mortality, but we observed no association of back pain with cancer mortality. Secondary back pain measures including back-related disability were associated with increased mortality risk that remained statistically significant in fully-adjusted models. CONCLUSION: While frequent persistent back pain was not independently associated with mortality in older men, additional secondary disabling back pain phenotypes were independently associated with increased mortality. Future investigations should evaluate whether improvements in disabling back pain effect general health and well-being or mortality.

Sleep-wake behaviors associated with cognitive performance in middle-aged participants of the Hispanic Community Health Study/Study of Latinos.

OBJECTIVES: Many sleep-wake behaviors have been associated with cognition. We examined a panel of sleep-wake/activity characteristics to determine which are most robustly related to having low cognitive performance in midlife. Secondarily, we evaluate the predictive utility of sleep-wake measures to screen for low cognitive performance. METHODS: The outcome was low cognitive performance defined as being >1 standard deviation below average age/sex/education internally normalized composite cognitive performance levels assessed in the Hispanic Community Health Study/Study of Latinos. Analyses included 1006 individuals who had sufficient sleep-wake measurements about 2years later (mean age=54.9, standard deviation= 5.1; 68.82% female). We evaluated associations of 31 sleep-wake variables with low cognitive performance using separate logistic regressions. RESULTS: In individual models, the strongest sleep-wake correlates of low cognitive performance were measures of weaker and unstable 24-hour rhythms; greater 24-hour fragmentation; longer time-in-bed; and lower rhythm amplitude. One standard deviation worse on these sleep-wake factors was associated with ∼20%-30% greater odds of having low cognitive performance. In an internally cross-validated prediction model, the independent correlates of low cognitive performance were: lower Sleep Regularity Index scores; lower pseudo-F statistics (modellability of 24-hour rhythms); lower activity rhythm amplitude; and greater time in bed. Area under the curve was low/moderate (64%) indicating poor predictive utility. CONCLUSION: The strongest sleep-wake behavioral correlates of low cognitive performance were measures of longer time-in-bed and irregular/weak rhythms. These sleep-wake assessments were not useful to identify previous low cognitive performance. Given their potential modifiability, experimental trials could test if targeting midlife time-in-bed and/or irregular rhythms influences cognition.

Association of Disrupted Delta Wave Activity During Sleep With Long-Term Cardiovascular Disease and Mortality.

BACKGROUND: Delta wave activity is a prominent feature of deep sleep, which is significantly associated with sleep quality. OBJECTIVES: The authors hypothesized that delta wave activity disruption during sleep could predict long-term cardiovascular disease (CVD) and CVD mortality risk. METHODS: The authors used a comprehensive power spectral entropy-based method to assess delta wave activity during sleep based on overnight polysomnograms in 4,058 participants in the SHHS (Sleep Heart Health Study) and 2,193 participants in the MrOS (Osteoporotic Fractures in Men Study) Sleep study. RESULTS: During 11.0 ± 2.8 years of follow-up in SHHS, 729 participants had incident CVD and 192 participants died due to CVD. During 15.5 ± 4.4 years of follow-up in MrOS, 547 participants had incident CVD, and 391 died due to CVD. In multivariable Cox regression models, lower delta wave entropy during sleep was associated with higher risk of coronary heart disease (SHHS: HR: 1.46; 95% CI: 1.02-2.06; P = 0.03; MrOS: HR: 1.79; 95% CI: 1.17-2.73; P < 0.01), CVD (SHHS: HR: 1.60; 95% CI: 1.21-2.11; P < 0.01; MrOS: HR: 1.43; 95% CI: 1.00-2.05; P = 0.05), and CVD mortality (SHHS: HR: 1.94; 95% CI: 1.18-3.18; P < 0.01; MrOS: HR: 1.66; 95% CI: 1.12-2.47; P = 0.01) after adjusting for covariates. The Shapley Additive Explanations method indicates that low delta wave entropy was more predictive of coronary heart disease, CVD, and CVD mortality risks than conventional sleep parameters. CONCLUSIONS: The results suggest that delta wave activity disruption during sleep may be a useful metric to identify those at increased risk for CVD and CVD mortality.

Sleep Duration Polygenic Risk and Phenotype: Associations with Biomarkers of Accelerated Aging in the Baltimore Longitudinal Study of Aging.

We sought to explore whether genetic risk for, and self-reported, short sleep are associated with biological aging and whether age and sex moderate these associations. Participants were a subset of individuals from the Baltimore Longitudinal Study of Aging who had complete data on self-reported sleep (n = 567) or genotype (n = 367). Outcomes included: Intrinsic Horvath age, Hannum age, PhenoAge, GrimAge, and DNAm-based estimates of plasminogen activator inhibitor-1 (PAI-1) and granulocyte count. Results demonstrated that polygenic risk for short sleep was positively associated with granulocyte count; compared to those reporting <6 hr sleep, those reporting >7 hr demonstrated faster PhenoAge and GrimAge acceleration and higher estimated PAI-1. Polygenic risk for short sleep and self-reported sleep duration interacted with age and sex in their associations with some of the outcomes. Findings highlight that polygenic risk for short sleep and self-reported long sleep is associated with variation in the epigenetic landscape and subsequently aging.

Stressful life events and low back pain in older men: A cross-sectional and prospective analysis using data from the MrOS study.

BACKGROUND: Stressful life events, such as loss of a partner, loss of a pet or financial problems, are more common with increasing age and may impact the experience of pain. The aim of the current study is to determine the cross-sectional and prospective association between stressful life events and low back pain reporting in the Osteoporotic Fracture in Men Study, a cohort of older men aged ≥65 years. METHODS: At a study visit (March 2005-May 2006), 5149 men reported whether they had experienced a stressful life event or low back pain in the prior 12 months. Following that visit, data on low back pain patients were gathered through triannual questionnaires every 4 months for 1 year. Multivariable logistic regression analyses estimated the association of stressful life events with recent past low back pain or future low back pain. RESULTS: N = 2930, (57%) men reported at least one stressful life event. The presence of a stressful life event was associated with greater odds of any low back pain (OR = 1.42 [1.26-1.59]) and activity-limiting low back pain (OR = 1.74 [1.50-2.01]) in the same period and of any low back pain (OR = 1.56 [1.39-1.74]) and frequent low back pain (OR = 1.80 [1.55-2.08]) in the following year. CONCLUSION: In this cohort of men, the presence of stressful life events increased the likelihood of reporting past and future low back pain. SIGNIFICANCE: Stressful life events such as accident or illness to a partner are common in later life and may impact the experience of pain. We present cross-sectional and prospective data highlighting a consistent association between stressful life events and low back pain in older men. Further, there is evidence to suggest that this relationship is upregulated by an individual's living situation. This information may be used to strengthen a biopsychosocial perspective of an individual's pain experience.

Accelerometer-assessed sleep and decline in physical function in older men.

OBJECTIVES: Assess the prospective association of actigraphically measured sleep with self-report and objective measures of physical function among community-dwelling older men. METHODS: Participants were (n = 1496) men aged ≥65 years from the Osteoporotic Fractures in Men Study and ancillary sleep study who were followed up at 4 years for physical function outcomes. Sleep predictors included baseline total sleep time (<6, 6-8 hours [reference], >8 hours), sleep efficiency (<80% or ≥80% [reference]), wake after sleep onset (<90 [reference] or ≥90 minutes), and sleep onset latency (<30 [reference] or ≥30 minutes), measured by wrist actigraphy. Outcomes included self-reported difficulties in mobility and instrumental activities of daily living and objective measures of physical performance (time to complete chair stands, gait speed, grip strength, best narrow walk pace). Multivariable regression models estimated associations between the sleep predictors and change in physical function at follow-up, adjusting for demographic and health-related variables. RESULTS: Participants with short average baseline total sleep time (<6 hours) had significantly greater slowing in their walking speed from baseline to follow-up. Participants with long baseline sleep onset latency (≥30 minutes) had significant increases in mobility difficulties and time to complete chair stands. Sleep efficiency and wake after sleep onset were not significantly associated with any outcomes. No sleep predictors were associated with change in instrumental activities of daily living. CONCLUSIONS: These findings add to the body of evidence showing links between poor sleep and subsequent declines in physical function. Further experimental research is needed to understand the mechanisms at play.

Redefining Comorbid Insomnia and Sleep Apnea: The Association of Sleep Breathing Impairment and Insomnia with Incident Diabetes.

Rationale: Obstructive sleep apnea (OSA) is a prevalent sleep disorder that is frequently comorbid with insomnia and often accompanied by metabolic diseases such as type 2 diabetes. Although the apnea-hypopnea index (AHI) is currently the diagnostic criterion for gauging the severity of OSA, the AHI has not consistently predicted incident diabetes. Objectives: To test whether a combined insomnia-OSA (COMISA) phenotype based on comorbid insomnia and sleep breathing impairment index (COMISA-SBII) predicts incident diabetes and to compare the association with an AHI definition of COMISA (COMISA-AHI) in the MrOS (Osteoporotic Fractures in Men) study. Methods: The study samples came from participants in the MrOS sleep study without diabetes at their baseline examination. The SBII was derived as the product of the duration of each respiratory event (apnea and hypopnea) and the accompanying desaturation area from baseline unattended polysomnography. A subgroup of individuals classified as having comorbid insomnia (difficulties falling asleep, waking up in the middle of the night and/or early morning awakenings >15 times per month, and daytime impairments) and sleep breathing impairment (greater than 50th percentile of SBII) were identified at baseline. The primary outcome was incident diabetes during the follow-up visits. Cox proportional models were built to assess the adjusted hazard ratios of COMISA-AHI and COMISA-SBII. Prediction model performances of incident diabetes were compared across different models. Results: A total of 2,365 men (mean age, 76 yr) without diabetes at baseline were included. During a median follow-up of 10.0 years, diabetes developed in 181. After adjusting for demographic characteristics, comorbidities, and behavioral risk factors, participants with COMISA-SBII had a higher risk of incident diabetes (hazard ratio, 1.82; 95% confidence interval, 1.15-2.89) than those without sleep disorders (those with an SBII ⩽13.17 and no insomnia). The result remained significant in the risk competing model. Compared with COMISA-AHI, the addition of COMISA-SBII to a crude model with established risk factors significantly improved the predictive value of incident diabetes. Conclusions: COMISA-SBII, but not COMISA-AHI, predicted incident diabetes after accounting for multiple covariates in a cohort of older men. A comorbid insomnia phenotype based on SBII plus insomnia symptoms may be an important clinical subtype.

Multivariate prediction of cognitive performance from the sleep electroencephalogram.

Human cognitive performance is a key function whose biological foundations have been partially revealed by genetic and brain imaging studies. The sleep electroencephalogram (EEG) is tightly linked to structural and functional features of the central nervous system and serves as another promising biomarker. We used data from MrOS, a large cohort of older men and cross-validated regularized regression to link sleep EEG features to cognitive performance in cross-sectional analyses. In independent validation samples 2.5-10% of variance in cognitive performance can be accounted for by sleep EEG features, depending on the covariates used. Demographic characteristics account for more covariance between sleep EEG and cognition than health variables, and consequently reduce this association by a greater degree, but even with the strictest covariate sets a statistically significant association is present. Sigma power in NREM and beta power in REM sleep were associated with better cognitive performance, while theta power in REM sleep was associated with worse performance, with no substantial effect of coherence and other sleep EEG metrics. Our findings show that cognitive performance is associated with the sleep EEG (r = 0.283), with the strongest effect ascribed to spindle-frequency activity. This association becomes weaker after adjusting for demographic (r = 0.186) and health variables (r = 0.155), but its resilience to covariate inclusion suggest that it also partially reflects trait-like differences in cognitive ability.

Social activity diversity as a lifestyle factor to alleviate loneliness and chronic pain.

OBJECTIVE: This study examined whether social activity diversity, a novel concept indicating an active social lifestyle, is associated with lower subsequent loneliness, and decreased loneliness is further associated with less chronic pain over time. METHODS: 2528 adults from the Midlife in the United States Study (Mage = 54 yrs) provided data at baseline (2004-2009) and 9 years later. Social activity diversity was operationalized by Shannon's entropy that captures the variety and evenness of engagement across 13 social activities (0-1). Participants reported feelings of loneliness (1-5), presence of any chronic pain (yes/no), the degree of chronic pain-related interference (0-10), and the number of chronic pain locations. Indirect associations of social activity diversity with chronic pain through loneliness were evaluated, adjusting for sociodemographics, living alone, and chronic conditions. RESULTS: Higher social activity diversity at baseline (B = -0.21, 95%CI = [-0.41, -0.02]) and an increase in social activity diversity over time (B = -0.24, 95%CI = [-0.42, -0.06]) were associated with lower loneliness 9 years later. An increase in loneliness was associated with 24% higher risk of any chronic pain (95%CI = [1.11, 1.38]), greater chronic pain-related interference (B = 0.36, 95%CI = [0.14, 0.58]), and 17% increase in the number of chronic pain locations (95%CI = [1.10, 1.25]) at the follow-up, after controlling for corresponding chronic pain at baseline and covariates. Social activity diversity was not directly was associated with chronic pain, but there were indirect associations through its association with loneliness. CONCLUSION: Diversity in social life may be associated with decreased loneliness, which in turn, may be associated with less chronic pain, two of the prevalent concerns in adulthood.

Sleep Apnea Physiological Burdens and Cardiovascular Morbidity and Mortality.

Rationale: Obstructive sleep apnea is characterized by frequent reductions in ventilation, leading to oxygen desaturations and/or arousals. Objectives: In this study, association of hypoxic burden with incident cardiovascular disease (CVD) was examined and compared with that of "ventilatory burden" and "arousal burden." Finally, we assessed the extent to which the ventilatory burden, visceral obesity, and lung function explain variations in hypoxic burden. Methods: Hypoxic, ventilatory, and arousal burdens were measured from baseline polysomnograms in the Multi-Ethnic Study of Atherosclerosis (MESA) and the Osteoporotic Fractures in Men (MrOS) studies. Ventilatory burden was defined as event-specific area under ventilation signal (mean normalized, area under the mean), and arousal burden was defined as the normalized cumulative duration of all arousals. The adjusted hazard ratios for incident CVD and mortality were calculated. Exploratory analyses quantified contributions to hypoxic burden of ventilatory burden, baseline oxygen saturation as measured by pulse oximetry, visceral obesity, and spirometry parameters. Measurements and Main Results: Hypoxic and ventilatory burdens were significantly associated with incident CVD (adjusted hazard ratio [95% confidence interval] per 1 SD increase in hypoxic burden: MESA, 1.45 [1.14, 1.84]; MrOS, 1.13 [1.02, 1.26]; ventilatory burden: MESA, 1.38 [1.11, 1.72]; MrOS, 1.12 [1.01, 1.25]), whereas arousal burden was not. Similar associations with mortality were also observed. Finally, 78% of variation in hypoxic burden was explained by ventilatory burden, whereas other factors explained only <2% of variation. Conclusions: Hypoxic and ventilatory burden predicted CVD morbidity and mortality in two population-based studies. Hypoxic burden is minimally affected by measures of adiposity and captures the risk attributable to ventilatory burden of obstructive sleep apnea rather than a tendency to desaturate.

Sleep Architecture, Obstructive Sleep Apnea, and Cognitive Function in Adults.

Importance: Good sleep is essential for health, yet associations between sleep and dementia risk remain incompletely understood. The Sleep and Dementia Consortium was established to study associations between polysomnography (PSG)-derived sleep and the risk of dementia and related cognitive and brain magnetic resonance imaging endophenotypes. Objective: To investigate association of sleep architecture and obstructive sleep apnea (OSA) with cognitive function in the Sleep and Dementia Consortium. Design, Setting, and Participants: The Sleep and Dementia Consortium curated data from 5 population-based cohorts across the US with methodologically consistent, overnight, home-based type II PSG and neuropsychological assessments over 5 years of follow-up: the Atherosclerosis Risk in Communities study, Cardiovascular Health Study, Framingham Heart Study (FHS), Osteoporotic Fractures in Men Study, and Study of Osteoporotic Fractures. Sleep metrics were harmonized centrally and then distributed to participating cohorts for cohort-specific analysis using linear regression; study-level estimates were pooled in random effects meta-analyses. Results were adjusted for demographic variables, the time between PSG and neuropsychological assessment (0-5 years), body mass index, antidepressant use, and sedative use. There were 5946 participants included in the pooled analyses without stroke or dementia. Data were analyzed from March 2020 to June 2023. Exposures: Measures of sleep architecture and OSA derived from in-home PSG. Main Outcomes and Measures: The main outcomes were global cognitive composite z scores derived from principal component analysis, with cognitive domains investigated as secondary outcomes. Higher scores indicated better performance. Results: Across cohorts, 5946 adults (1875 females [31.5%]; mean age range, 58-89 years) were included. The median (IQR) wake after sleep onset time ranged from 44 (27-73) to 101 (66-147) minutes, and the prevalence of moderate to severe OSA ranged from 16.9% to 28.9%. Across cohorts, higher sleep maintenance efficiency (pooled ß per 1% increase, 0.08; 95% CI, 0.03 to 0.14; P < .01) and lower wake after sleep onset (pooled ß per 1-min increase, -0.07; 95% CI, -0.13 to -0.01 per 1-min increase; P = .02) were associated with better global cognition. Mild to severe OSA (apnea-hypopnea index [AHI] ≥5) was associated with poorer global cognition (pooled ß, -0.06; 95% CI, -0.11 to -0.01; P = .01) vs AHI less than 5; comparable results were found for moderate to severe OSA (pooled ß, -0.06; 95% CI, -0.11 to -0.01; P = .02) vs AHI less than 5. Differences in sleep stages were not associated with cognition. Conclusions and Relevance: This study found that better sleep consolidation and the absence of OSA were associated with better global cognition over 5 years of follow-up. These findings suggest that the role of interventions to improve sleep for maintaining cognitive function requires investigation.

AI-Driven sleep staging from actigraphy and heart rate.

Sleep is an important indicator of a person's health, and its accurate and cost-effective quantification is of great value in healthcare. The gold standard for sleep assessment and the clinical diagnosis of sleep disorders is polysomnography (PSG). However, PSG requires an overnight clinic visit and trained technicians to score the obtained multimodality data. Wrist-worn consumer devices, such as smartwatches, are a promising alternative to PSG because of their small form factor, continuous monitoring capability, and popularity. Unlike PSG, however, wearables-derived data are noisier and far less information-rich because of the fewer number of modalities and less accurate measurements due to their small form factor. Given these challenges, most consumer devices perform two-stage (i.e., sleep-wake) classification, which is inadequate for deep insights into a person's sleep health. The challenging multi-class (three, four, or five-class) staging of sleep using data from wrist-worn wearables remains unresolved. The difference in the data quality between consumer-grade wearables and lab-grade clinical equipment is the motivation behind this study. In this paper, we present an artificial intelligence (AI) technique termed sequence-to-sequence LSTM for automated mobile sleep staging (SLAMSS), which can perform three-class (wake, NREM, REM) and four-class (wake, light, deep, REM) sleep classification from activity (i.e., wrist-accelerometry-derived locomotion) and two coarse heart rate measures-both of which can be reliably obtained from a consumer-grade wrist-wearable device. Our method relies on raw time-series datasets and obviates the need for manual feature selection. We validated our model using actigraphy and coarse heart rate data from two independent study populations: the Multi-Ethnic Study of Atherosclerosis (MESA; N = 808) cohort and the Osteoporotic Fractures in Men (MrOS; N = 817) cohort. SLAMSS achieves an overall accuracy of 79%, weighted F1 score of 0.80, 77% sensitivity, and 89% specificity for three-class sleep staging and an overall accuracy of 70-72%, weighted F1 score of 0.72-0.73, 64-66% sensitivity, and 89-90% specificity for four-class sleep staging in the MESA cohort. It yielded an overall accuracy of 77%, weighted F1 score of 0.77, 74% sensitivity, and 88% specificity for three-class sleep staging and an overall accuracy of 68-69%, weighted F1 score of 0.68-0.69, 60-63% sensitivity, and 88-89% specificity for four-class sleep staging in the MrOS cohort. These results were achieved with feature-poor inputs with a low temporal resolution. In addition, we extended our three-class staging model to an unrelated Apple Watch dataset. Importantly, SLAMSS predicts the duration of each sleep stage with high accuracy. This is especially significant for four-class sleep staging, where deep sleep is severely underrepresented. We show that, by appropriately choosing the loss function to address the inherent class imbalance, our method can accurately estimate deep sleep time (SLAMSS/MESA: 0.61±0.69 hours, PSG/MESA ground truth: 0.60±0.60 hours; SLAMSS/MrOS: 0.53±0.66 hours, PSG/MrOS ground truth: 0.55±0.57 hours;). Deep sleep quality and quantity are vital metrics and early indicators for a number of diseases. Our method, which enables accurate deep sleep estimation from wearables-derived data, is therefore promising for a variety of clinical applications requiring long-term deep sleep monitoring.

Relevance of cortical arousals for risk stratification in sleep apnea: a 3 cohort analysis.

STUDY OBJECTIVES: There is uncertainty on best approaches for defining apnea-hypopnea events. To clarify the contributions of desaturation vs arousal to defining hypopneas, we examined the associations of events with desaturation (≥ 3%) but not arousal (apnea-hypopnea index [AHI]≥3%Only) vs events with arousals but no desaturation (AHIArOnly) with obstructive sleep apnea-related comorbidities and incident cardiovascular disease across multiple cohorts. METHODS: In the Sleep Heart Health Study (n = 5,473), the Multi-Ethnic Study of Atherosclerosis (n = 1,904), and the Osteoporotic Fractures in Men Study (n = 2,685), we examined the independent associations of AHI≥3%Only and AHIArOnly with hypertension, diabetes, and daytime sleepiness, and incident cardiovascular disease. RESULTS: After adjusting for covariates and AHI based on events with electroencephalogram arousal (regardless of desaturation), AHI≥3%Only was associated with hypertension in Sleep Heart Health Study (odds ratio: 1.12; 95% confidence interval: 1.04,1.21), per 1 standard deviation increase). Similar associations were observed in the Multi-Ethnic Study of Atherosclerosis and Osteoporotic Fractures in Men Study, as well as for associations with diabetes (odds ratio: 1.30; 1.09,1.54, and 1.25; 1.07,1.47, respectively), sleepiness (odds ratio: 1.19; 1.00,1.41; and 1.17; 1.01-1.35), and incident cardiovascular disease (hazard ratio: 1.37; 1.05,1.77 and 1.14; 1.00,1.29). In contrast, after adjusting for events with desaturation (regardless of arousal), AHIArOnly was unassociated with these outcomes. In Sleep Heart Health Study, greater baseline obstructive sleep apnea severity was associated with a reduction in arousal frequency over 5 years (P < .0001). CONCLUSIONS: In middle-aged and older individuals, addition of events with arousals does not improve the strength of associations with comorbidities or incident cardiovascular disease. Research is needed to understand generalizability to younger individuals and the mechanistic role of arousals in obstructive sleep apnea. CITATION: Azarbarzin A, Sands SA, Han S, et al. Relevance of cortical arousals for risk stratification in sleep apnea: a 3 cohort analysis. J Clin Sleep Med. 2023;19(8):1475-1484.

Acupuncture for chronic low back pain in older adults: Design and protocol for the BackInAction pragmatic clinical trial.

BACKGROUND: Back pain prevalence and burden increase with age; approximately one-third of U.S. adults 65 years of age and older experience lower back pain (LBP). For chronic low back pain (cLBP), typically defined as lasting three months or longer, many treatments for younger adults may be inappropriate for older adults given their greater prevalence of comorbidities with attendant polypharmacy. While acupuncture has been demonstrated to be safe and effective for cLBP in adults overall, few studies of acupuncture have either included or focused on adults ≥65 years old. METHODS: The BackInAction study is a pragmatic, multi-site, three-arm, parallel-groups randomized controlled trial designed to test the effectiveness of acupuncture needling for improving back pain-related disability among 807 older adults ≥65 years old with cLBP. Participants are randomized to standard acupuncture (SA; up to 15 treatment sessions across 12 weeks), enhanced acupuncture (EA; SA during first 12 weeks and up to 6 additional sessions across the following 12 weeks), and usual medical care (UMC) alone. Participants are followed for 12 months with study outcomes assessed monthly with the primary outcome timepoint at 6 months. DISCUSSION: The BackInAction study offers an opportunity to further understand the effectiveness, dose-dependence, and safety of acupuncture in a Medicare population. Additionally, study results may encourage broader adoption of more effective, safer, and more satisfactory options to the continuing over-reliance on opioid- and invasive medical treatments for cLBP among older adults. TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT04982315. Clinical trial registration date: July 29, 2021.